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/machine learning and biological or clinical applications Solid programming skills (e.g. Python, R, or similar) Ability to work independently and collaboratively in an international, interdisciplinary
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field. Experience and skills Experience in R&D projects related to the field of data analytics and modelling in Energy, Environment, Mobility or Climate, among others. Strong programming skills in Python
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the contract period; 3. Experience in implementing neural rendering models; 4. Practical experience in the analysis of scientific data; 5. Proficiency in programming with Python; 6. Familiarity with the neural
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language FORTRAN is a plus. · Knowledge of Python and Git is a plus. · You have a proven ability to write high-quality scientific papers. · You like working in a team, explaining
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aeroacoustics (wind tunnel testing, microphone arrays) MATLAB / Python for signal processing CFD software (ANSYS Fluent, OpenFOAM) FEM softwares (COMSOL, ACTRAN) Spectral analysis (FFT, wavelets) Behavioural
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skills in Python and experience with visualization data software considered an additional asset 2. Employment Conditions: • Recruitment for Assistant positions within project no. FENG.01.01-IP.01
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software such as STATA, R, or Python. Potential to publish articles in high quality international academic journals. Ability to work in teams as well as independently. Written and spoken proficiency in
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interpersonal skills with team-oriented mind-set Interest in setting-up new academic and industrial collaborations. Advanced programming skills (e.g. Python) would be considered as an asset. Language skills
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 days ago
. Preference will be given to candidates with aptitude for working in disaster risk management environments supported with GIS and Machine Learning, basic programming skills (e.g., Python, R, or similar
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such as R or Python Manage the full biomarker panning workflow, including strategic planning, operation oversight, biomolecular sample characterisation using UHPLC glycan analysis, mass spectrometry and